{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.12.0\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras import Sequential\n",
    "from tensorflow.keras.layers import Flatten, Dense, Dropout, BatchNormalization\n",
    "from tensorflow.keras.layers import Conv2D, MaxPool2D\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler, LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "data=pd.read_csv('deva walk and fall.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>gFx</th>\n",
       "      <th>gFy</th>\n",
       "      <th>gFz</th>\n",
       "      <th>TgF</th>\n",
       "      <th>activity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.607</td>\n",
       "      <td>0.5201</td>\n",
       "      <td>0.7910</td>\n",
       "      <td>0.1875</td>\n",
       "      <td>0.965</td>\n",
       "      <td>walking</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.609</td>\n",
       "      <td>0.5155</td>\n",
       "      <td>0.7976</td>\n",
       "      <td>0.1844</td>\n",
       "      <td>0.967</td>\n",
       "      <td>walking</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.611</td>\n",
       "      <td>0.5204</td>\n",
       "      <td>0.7979</td>\n",
       "      <td>0.1873</td>\n",
       "      <td>0.971</td>\n",
       "      <td>walking</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.613</td>\n",
       "      <td>0.5189</td>\n",
       "      <td>0.8037</td>\n",
       "      <td>0.1880</td>\n",
       "      <td>0.975</td>\n",
       "      <td>walking</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.615</td>\n",
       "      <td>0.5238</td>\n",
       "      <td>0.8084</td>\n",
       "      <td>0.1900</td>\n",
       "      <td>0.982</td>\n",
       "      <td>walking</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.617</td>\n",
       "      <td>0.5228</td>\n",
       "      <td>0.8113</td>\n",
       "      <td>0.1929</td>\n",
       "      <td>0.984</td>\n",
       "      <td>walking</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5.619</td>\n",
       "      <td>0.5265</td>\n",
       "      <td>0.8133</td>\n",
       "      <td>0.1976</td>\n",
       "      <td>0.989</td>\n",
       "      <td>walking</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.620</td>\n",
       "      <td>0.5233</td>\n",
       "      <td>0.8147</td>\n",
       "      <td>0.1995</td>\n",
       "      <td>0.989</td>\n",
       "      <td>walking</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5.622</td>\n",
       "      <td>0.5199</td>\n",
       "      <td>0.8189</td>\n",
       "      <td>0.1995</td>\n",
       "      <td>0.990</td>\n",
       "      <td>walking</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5.624</td>\n",
       "      <td>0.5216</td>\n",
       "      <td>0.8164</td>\n",
       "      <td>0.2027</td>\n",
       "      <td>0.990</td>\n",
       "      <td>walking</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    time     gFx     gFy     gFz    TgF activity\n",
       "0  5.607  0.5201  0.7910  0.1875  0.965  walking\n",
       "1  5.609  0.5155  0.7976  0.1844  0.967  walking\n",
       "2  5.611  0.5204  0.7979  0.1873  0.971  walking\n",
       "3  5.613  0.5189  0.8037  0.1880  0.975  walking\n",
       "4  5.615  0.5238  0.8084  0.1900  0.982  walking\n",
       "5  5.617  0.5228  0.8113  0.1929  0.984  walking\n",
       "6  5.619  0.5265  0.8133  0.1976  0.989  walking\n",
       "7  5.620  0.5233  0.8147  0.1995  0.989  walking\n",
       "8  5.622  0.5199  0.8189  0.1995  0.990  walking\n",
       "9  5.624  0.5216  0.8164  0.2027  0.990  walking"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>gFx</th>\n",
       "      <th>gFy</th>\n",
       "      <th>gFz</th>\n",
       "      <th>TgF</th>\n",
       "      <th>activity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13376</th>\n",
       "      <td>2.884</td>\n",
       "      <td>0.3609</td>\n",
       "      <td>0.7661</td>\n",
       "      <td>0.5061</td>\n",
       "      <td>0.987</td>\n",
       "      <td>walk_fall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13377</th>\n",
       "      <td>2.890</td>\n",
       "      <td>0.3291</td>\n",
       "      <td>0.7622</td>\n",
       "      <td>0.4876</td>\n",
       "      <td>0.963</td>\n",
       "      <td>walk_fall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13378</th>\n",
       "      <td>2.892</td>\n",
       "      <td>0.3052</td>\n",
       "      <td>0.7532</td>\n",
       "      <td>0.4898</td>\n",
       "      <td>0.949</td>\n",
       "      <td>walk_fall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13379</th>\n",
       "      <td>2.893</td>\n",
       "      <td>0.2915</td>\n",
       "      <td>0.7453</td>\n",
       "      <td>0.5034</td>\n",
       "      <td>0.945</td>\n",
       "      <td>walk_fall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13380</th>\n",
       "      <td>2.894</td>\n",
       "      <td>0.2893</td>\n",
       "      <td>0.7392</td>\n",
       "      <td>0.5139</td>\n",
       "      <td>0.946</td>\n",
       "      <td>walk_fall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13381</th>\n",
       "      <td>2.897</td>\n",
       "      <td>0.2966</td>\n",
       "      <td>0.7324</td>\n",
       "      <td>0.5332</td>\n",
       "      <td>0.953</td>\n",
       "      <td>walk_fall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13382</th>\n",
       "      <td>2.901</td>\n",
       "      <td>0.3052</td>\n",
       "      <td>0.7236</td>\n",
       "      <td>0.5626</td>\n",
       "      <td>0.966</td>\n",
       "      <td>walk_fall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13383</th>\n",
       "      <td>2.906</td>\n",
       "      <td>0.3137</td>\n",
       "      <td>0.7150</td>\n",
       "      <td>0.5845</td>\n",
       "      <td>0.975</td>\n",
       "      <td>walk_fall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13384</th>\n",
       "      <td>2.907</td>\n",
       "      <td>0.3223</td>\n",
       "      <td>0.7170</td>\n",
       "      <td>0.5985</td>\n",
       "      <td>0.988</td>\n",
       "      <td>walk_fall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13385</th>\n",
       "      <td>2.907</td>\n",
       "      <td>0.3164</td>\n",
       "      <td>0.7172</td>\n",
       "      <td>0.6153</td>\n",
       "      <td>0.997</td>\n",
       "      <td>walk_fall</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        time     gFx     gFy     gFz    TgF   activity\n",
       "13376  2.884  0.3609  0.7661  0.5061  0.987  walk_fall\n",
       "13377  2.890  0.3291  0.7622  0.4876  0.963  walk_fall\n",
       "13378  2.892  0.3052  0.7532  0.4898  0.949  walk_fall\n",
       "13379  2.893  0.2915  0.7453  0.5034  0.945  walk_fall\n",
       "13380  2.894  0.2893  0.7392  0.5139  0.946  walk_fall\n",
       "13381  2.897  0.2966  0.7324  0.5332  0.953  walk_fall\n",
       "13382  2.901  0.3052  0.7236  0.5626  0.966  walk_fall\n",
       "13383  2.906  0.3137  0.7150  0.5845  0.975  walk_fall\n",
       "13384  2.907  0.3223  0.7170  0.5985  0.988  walk_fall\n",
       "13385  2.907  0.3164  0.7172  0.6153  0.997  walk_fall"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "time        0\n",
       "gFx         0\n",
       "gFy         0\n",
       "gFz         0\n",
       "TgF         0\n",
       "activity    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 13386 entries, 0 to 13385\n",
      "Data columns (total 6 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   time      13386 non-null  float64\n",
      " 1   gFx       13386 non-null  float64\n",
      " 2   gFy       13386 non-null  float64\n",
      " 3   gFz       13386 non-null  float64\n",
      " 4   TgF       13386 non-null  float64\n",
      " 5   activity  13386 non-null  object \n",
      "dtypes: float64(5), object(1)\n",
      "memory usage: 627.6+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['walking', 'walk_fall'], dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "activities = data['activity'].value_counts().index\n",
    "activities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "walking      9241\n",
       "walk_fall    4145\n",
       "Name: activity, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['activity'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_activity(activity, data):\n",
    "    fig, (ax0, ax1, ax2) = plt.subplots(nrows=3, figsize=(15, 7), sharex=True)\n",
    "    plot_axis(ax0, data['time'], data['gFx'], 'X-Axis')\n",
    "    plot_axis(ax1, data['time'], data['gFy'], 'Y-Axis')\n",
    "    plot_axis(ax2, data['time'], data['gFz'], 'Z-Axis')\n",
    "    plt.subplots_adjust(hspace=0.2)\n",
    "    fig.suptitle(activity)\n",
    "    plt.subplots_adjust(top=0.90)\n",
    "    plt.show()\n",
    "\n",
    "def plot_axis(ax, x, y, title):\n",
    "    ax.plot(x, y, 'g')\n",
    "    ax.set_title(title)\n",
    "    ax.xaxis.set_visible(False)\n",
    "    ax.set_ylim([min(y) - np.std(y), max(y) + np.std(y)])\n",
    "    ax.set_xlim([min(x), max(x)])\n",
    "    ax.grid(True)\n",
    "\n",
    "for activity in activities:\n",
    "    data_for_plot = data[(data['activity'] == activity)][:30]\n",
    "    plot_activity(activity, data_for_plot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gFx</th>\n",
       "      <th>gFy</th>\n",
       "      <th>gFz</th>\n",
       "      <th>TgF</th>\n",
       "      <th>activity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.5201</td>\n",
       "      <td>0.7910</td>\n",
       "      <td>0.1875</td>\n",
       "      <td>0.965</td>\n",
       "      <td>walking</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.5155</td>\n",
       "      <td>0.7976</td>\n",
       "      <td>0.1844</td>\n",
       "      <td>0.967</td>\n",
       "      <td>walking</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.5204</td>\n",
       "      <td>0.7979</td>\n",
       "      <td>0.1873</td>\n",
       "      <td>0.971</td>\n",
       "      <td>walking</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.5189</td>\n",
       "      <td>0.8037</td>\n",
       "      <td>0.1880</td>\n",
       "      <td>0.975</td>\n",
       "      <td>walking</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.5238</td>\n",
       "      <td>0.8084</td>\n",
       "      <td>0.1900</td>\n",
       "      <td>0.982</td>\n",
       "      <td>walking</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      gFx     gFy     gFz    TgF activity\n",
       "0  0.5201  0.7910  0.1875  0.965  walking\n",
       "1  0.5155  0.7976  0.1844  0.967  walking\n",
       "2  0.5204  0.7979  0.1873  0.971  walking\n",
       "3  0.5189  0.8037  0.1880  0.975  walking\n",
       "4  0.5238  0.8084  0.1900  0.982  walking"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = data.drop(['time'], axis = 1).copy()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8290, 5)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Walking = df[df['activity']=='walking'].head(4145).copy()\n",
    "fall = df[df['activity']=='walk_fall'].copy()\n",
    "\n",
    "balanced_data = pd.DataFrame()\n",
    "balanced_data = balanced_data.append([Walking, fall])\n",
    "balanced_data.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "walk_fall    4145\n",
       "walking      4145\n",
       "Name: activity, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "balanced_data['activity'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gFx</th>\n",
       "      <th>gFy</th>\n",
       "      <th>gFz</th>\n",
       "      <th>TgF</th>\n",
       "      <th>activity</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.5201</td>\n",
       "      <td>0.7910</td>\n",
       "      <td>0.1875</td>\n",
       "      <td>0.965</td>\n",
       "      <td>walking</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.5155</td>\n",
       "      <td>0.7976</td>\n",
       "      <td>0.1844</td>\n",
       "      <td>0.967</td>\n",
       "      <td>walking</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.5204</td>\n",
       "      <td>0.7979</td>\n",
       "      <td>0.1873</td>\n",
       "      <td>0.971</td>\n",
       "      <td>walking</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.5189</td>\n",
       "      <td>0.8037</td>\n",
       "      <td>0.1880</td>\n",
       "      <td>0.975</td>\n",
       "      <td>walking</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.5238</td>\n",
       "      <td>0.8084</td>\n",
       "      <td>0.1900</td>\n",
       "      <td>0.982</td>\n",
       "      <td>walking</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      gFx     gFy     gFz    TgF activity  label\n",
       "0  0.5201  0.7910  0.1875  0.965  walking      1\n",
       "1  0.5155  0.7976  0.1844  0.967  walking      1\n",
       "2  0.5204  0.7979  0.1873  0.971  walking      1\n",
       "3  0.5189  0.8037  0.1880  0.975  walking      1\n",
       "4  0.5238  0.8084  0.1900  0.982  walking      1"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label = LabelEncoder()\n",
    "balanced_data['label'] = label.fit_transform(balanced_data['activity'])\n",
    "balanced_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['walk_fall', 'walking'], dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label.classes_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = balanced_data[['gFx', 'gFy', 'gFz']]\n",
    "y = balanced_data['label']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gFx</th>\n",
       "      <th>gFy</th>\n",
       "      <th>gFz</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.461716</td>\n",
       "      <td>0.196431</td>\n",
       "      <td>0.056415</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.453301</td>\n",
       "      <td>0.211432</td>\n",
       "      <td>0.048275</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.462265</td>\n",
       "      <td>0.212114</td>\n",
       "      <td>0.055890</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.459521</td>\n",
       "      <td>0.225297</td>\n",
       "      <td>0.057728</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.468484</td>\n",
       "      <td>0.235980</td>\n",
       "      <td>0.062980</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        gFx       gFy       gFz  label\n",
       "0  0.461716  0.196431  0.056415      1\n",
       "1  0.453301  0.211432  0.048275      1\n",
       "2  0.462265  0.212114  0.055890      1\n",
       "3  0.459521  0.225297  0.057728      1\n",
       "4  0.468484  0.235980  0.062980      1"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler = StandardScaler()\n",
    "X = scaler.fit_transform(X)\n",
    "\n",
    "scaled_X = pd.DataFrame(data = X, columns = ['gFx', 'gFy', 'gFz'])\n",
    "scaled_X['label'] = y.values\n",
    "\n",
    "scaled_X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gFx</th>\n",
       "      <th>gFy</th>\n",
       "      <th>gFz</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8285</th>\n",
       "      <td>0.052863</td>\n",
       "      <td>0.063238</td>\n",
       "      <td>0.964205</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8286</th>\n",
       "      <td>0.068595</td>\n",
       "      <td>0.043236</td>\n",
       "      <td>1.041408</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8287</th>\n",
       "      <td>0.084144</td>\n",
       "      <td>0.023689</td>\n",
       "      <td>1.098916</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8288</th>\n",
       "      <td>0.099876</td>\n",
       "      <td>0.028235</td>\n",
       "      <td>1.135680</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8289</th>\n",
       "      <td>0.089083</td>\n",
       "      <td>0.028689</td>\n",
       "      <td>1.179796</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           gFx       gFy       gFz  label\n",
       "8285  0.052863  0.063238  0.964205      0\n",
       "8286  0.068595  0.043236  1.041408      0\n",
       "8287  0.084144  0.023689  1.098916      0\n",
       "8288  0.099876  0.028235  1.135680      0\n",
       "8289  0.089083  0.028689  1.179796      0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_X.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.stats as stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "Fs = 20\n",
    "frame_size = Fs*4 # 80\n",
    "hop_size = Fs*2 # 40"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((206, 80, 3), (206,))"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_frames(df, frame_size, hop_size):\n",
    "\n",
    "    N_FEATURES = 3\n",
    "\n",
    "    frames = []\n",
    "    labels = []\n",
    "    for i in range(0, len(df) - frame_size, hop_size):\n",
    "        x = df['gFx'].values[i: i + frame_size]\n",
    "        y = df['gFy'].values[i: i + frame_size]\n",
    "        z = df['gFz'].values[i: i + frame_size]\n",
    "        \n",
    "        # Retrieve the most often used label in this segment\n",
    "        label = stats.mode(df['label'][i: i + frame_size])[0][0]\n",
    "        frames.append([x, y, z])\n",
    "        labels.append(label)\n",
    "\n",
    "    # Bring the segments into a better shape\n",
    "    frames = np.asarray(frames).reshape(-1, frame_size, N_FEATURES)\n",
    "    labels = np.asarray(labels)\n",
    "\n",
    "    return frames, labels\n",
    "\n",
    "X, y = get_frames(scaled_X, frame_size, hop_size)\n",
    "\n",
    "X.shape, y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0, stratify = y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((164, 80, 3), (42, 80, 3))"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((80, 3), (80, 3))"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train[0].shape, X_test[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = X_train.reshape(164, 80, 3, 1)\n",
    "X_test = X_test.reshape(42, 80, 3, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((80, 3, 1), (80, 3, 1))"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train[0].shape, X_test[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(Conv2D(16, (2, 2), activation = 'relu', input_shape = X_train[0].shape))\n",
    "model.add(Dropout(0.1))\n",
    "\n",
    "model.add(Conv2D(32, (2, 2), activation='relu'))\n",
    "model.add(Dropout(0.2))\n",
    "\n",
    "model.add(Flatten())\n",
    "\n",
    "model.add(Dense(64, activation = 'relu'))\n",
    "model.add(Dropout(0.5))\n",
    "\n",
    "model.add(Dense(6, activation='softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "learning_rate = 0.001\n",
    "model.compile(optimizer=Adam(learning_rate), loss = 'sparse_categorical_crossentropy', metrics = ['accuracy'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 164 samples, validate on 42 samples\n",
      "Epoch 1/10\n",
      "164/164 [==============================] - 1s 3ms/step - loss: 1.1923 - acc: 0.6707 - val_loss: 0.4621 - val_acc: 1.0000\n",
      "Epoch 2/10\n",
      "164/164 [==============================] - 0s 267us/step - loss: 0.4101 - acc: 0.8902 - val_loss: 0.1227 - val_acc: 0.9762\n",
      "Epoch 3/10\n",
      "164/164 [==============================] - 0s 298us/step - loss: 0.1704 - acc: 0.9634 - val_loss: 0.0617 - val_acc: 0.9762\n",
      "Epoch 4/10\n",
      "164/164 [==============================] - 0s 243us/step - loss: 0.2059 - acc: 0.9695 - val_loss: 0.0388 - val_acc: 1.0000\n",
      "Epoch 5/10\n",
      "164/164 [==============================] - 0s 237us/step - loss: 0.1548 - acc: 0.9573 - val_loss: 0.0270 - val_acc: 1.0000\n",
      "Epoch 6/10\n",
      "164/164 [==============================] - 0s 249us/step - loss: 0.1115 - acc: 0.9695 - val_loss: 0.0195 - val_acc: 1.0000\n",
      "Epoch 7/10\n",
      "164/164 [==============================] - 0s 243us/step - loss: 0.1596 - acc: 0.9756 - val_loss: 0.0152 - val_acc: 1.0000\n",
      "Epoch 8/10\n",
      "164/164 [==============================] - 0s 249us/step - loss: 0.0616 - acc: 0.9878 - val_loss: 0.0140 - val_acc: 1.0000\n",
      "Epoch 9/10\n",
      "164/164 [==============================] - 0s 225us/step - loss: 0.0734 - acc: 0.9817 - val_loss: 0.0137 - val_acc: 1.0000\n",
      "Epoch 10/10\n",
      "164/164 [==============================] - 0s 225us/step - loss: 0.0555 - acc: 0.9756 - val_loss: 0.0200 - val_acc: 1.0000\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(X_train, y_train, epochs = 10, validation_data= (X_test, y_test), verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_learningCurve(history, epochs):\n",
    "  # Plot training & validation accuracy values\n",
    "  epoch_range = range(1, epochs+1)\n",
    "  plt.plot(epoch_range, history.history['acc'])\n",
    "  plt.plot(epoch_range, history.history['val_acc'])\n",
    "  plt.title('Model accuracy')\n",
    "  plt.ylabel('Accuracy')\n",
    "  plt.xlabel('Epoch')\n",
    "  plt.legend(['Train', 'Val'], loc='upper left')\n",
    "  plt.show()\n",
    "\n",
    "  # Plot training & validation loss values\n",
    "  plt.plot(epoch_range, history.history['loss'])\n",
    "  plt.plot(epoch_range, history.history['val_loss'])\n",
    "  plt.title('Model loss')\n",
    "  plt.ylabel('Loss')\n",
    "  plt.xlabel('Epoch')\n",
    "  plt.legend(['Train', 'Val'], loc='upper left')\n",
    "  plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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o3Te/+U02bdrE97///eAiu7KpUDoZeg5wwWsu55avfIn6sxYf/ZvtcXjHL44/6MIKqDs7PQvpckjdff3sbu8+ZL/6rvYuWvZ30z+iOyQvO2vo12r97EqunjabRTMrOHVaeVJ/vU4kZkZ5US7lRbksmHLoH2QHe/qGEkj86KzqkuS00pQgkmz58uWsWLFiWIJYsWIFX/va14bvaFmQXxp0MeWXHNvEdbmFcNIlxxmxpKP93X1xvzxf/ut/V3sXsQM9LzuuOC976CKzV8yrefnInLICqorzMmZUTrorysthbm0Jc2tLxuX9lCCS7C1veQuf/exn6enpIS8vjy1btrBjxw7uvvtuPv7xj9PZ2clb3vIWvvCFL0QdasoYHD2SZTCzqmjCn5wGBpymPfvZ1to5NLZ/Z9yJf1d711ANIF5lUS5TyguZWl7A6XUVo47tP5ahpDJxTJwE8fsbYNfTY/uaU06Dy7962F2qqqpYsmQJv//971m2bBkrVqzgbW97G5/5zGeoqqqiv7+fiy++mPXr13P66aePbXwpZnD8+bCLmNq72dUxfBz64JWzEJzkzphRMdTHvaiugvKizD6pjSwCP7Wtnf1xCcAMJpXmM6WsgLm1wQihkePyJ5cVUJCr7h85PhMnQURosJtpMEH8+Mc/5p577uG2226jr6+PnTt3smHDhrROEAMDTuvBnpf6s+MuQIrv7jjYM/oVrFPK86mrLOLs2VVDJ7vuvgGe2hacKP/fsy1D483n1haHQyYrWTyjgvlTSiMfDnisunr72bCzg7Vb24auGWje+1IReMGUUt6weBqLZlQyt7aYKWUF1KbA8EeZGCZOgjjCL/1kWrZsGddffz1r1qzh4MGDVFVVccstt7Bq1SoqKyu5+uqr6erqiiy+I+nrH+DFfd0jTvad7OroHpYERs6Bk51lTC7NZ0p5ASdPKeOCkyYNdW0M/sqdXHb4OXDecU5wr/F9Xb3DLrz687Mt/HLNdiCYsO206eUsnlk5NNZ+anlh8r6QY+TubG09OCwZbNjZMfS9TS0vYPHMCt61dBaLZ1aqCCyRmzgJIkIlJSVceOGFvOc972H58uV0dHRQXFxMeXk5u3fv5ve///0h7wMx3pr3HuT2v25he9vBoS6fln3djBjYQn5O1tDJvn5W5VBf9+A8NmM9i2ZpQS7nn1jD+SfWAMHJtnlv59AcPmu37eWO/91CT38wlnxyWf5QK2PRjApOrysf93l02jt7Wd/cNpQQ1m1rozUsFBfmZnN6XTnvecUcFs+oZPHMCiaXFYxrfCJHogQxTpYvX84b3/hGVqxYwYIFC1i8eDELFixgxowZnH/++VGHBwRD6N5zxyq2xA4yq6qIKeUFzJ9SypTywmGFzanlBZHPw29mzKgqYkZVEa8/YxoQDOPcuHMf67aGk79ta+PBZ3YDQWvmpMmlwcVbMyo4c2YFc2tKxmzMfl//AJt27xvWOmhsOTC0fd6kEi5eMGkoYZ00uYQcdRNJitN03xnkeD6vu3P9f63jN0/t4GfvOYdXzKsZ4+iiEdvfzVPNg62M4OS9ryso+JYW5HBGXcVQ0lg0o4LqksSmPNnV3jU06dzabW083dw+NENodXHe0OstnlnJ6TPKKdNoIUlRmu5bjujnj2/l1+t28InXnJQxyQGguiSfixZM5qIFk4HBIaMHht2A5nuPNg5dHDazqmgoYSyeWcnJU0sZGICnt7cPO2ZXR1AzysvOYuG0Mt5+9gwWzwxGW82oGr+J50SSSQlCeGpbG1/87QYunF/Lhy48MepwkioryzhxUgknTirhrfUzgKBr7emhAngbjze18pt1O4AgAfS7D0sgS+ZUDSWRhdPKyM9RIVkyU8YnCHefEL/mjrWrcO+BHv7lrjXUlubzjbcvmpDz6BTl5XDO3GrOmVs9tG5ne2dw74LmNvKys466C0okEyQ1QZjZZcC3gGzgR+7+1RHbZwG3A7VAK3CluzeH2/qBwSvbtrr764/2/QsKCojFYlRXV2d0knB3YrEYBQVHNwpmYMC5/p51tOzr5t5rl1JRlLmzbh6tqeWFTD2tkMtP021TZeJKWoIws2zgu8BrgGZglZnd7+4b4na7BbjT3X9qZhcBXwH+OdzW6e6LjieGuro6mpubaWlpOZ6XSQsFBQXU1dUd1THfeWQzj25q4UtvOJXT6yqSE5iIpK1ktiCWAJvdvQnAzFYAy4D4BLEQ+Hi4/Ajw67EMIDc3lzlz5ozlS2aMPz/bwjf+9CxvXDydd4YXo4mIxEvmQOzpwLa4583hunhPAW8Kl98IlJrZYEdwgZmtNrPHzOwNo72BmV0T7rN6IrQSxsqOtk4+umIt8yaVcPMbT83o7jcROXZRX6nzSeBVZrYWeBWwHRicrGdWODb3HcA3zeyEkQe7+23uXu/u9bW1teMWdDrr6RvgX+5aQ2+/8/0rzxr3q4tFJH0k8+ywHZgR97wuXDfE3XcQtiDMrAR4s7u3hdu2h3+bzOxRYDHQmMR4J4QvP7CRddva+N47z+SEcZpTXkTSUzJbEKuAeWY2x8zygCuA++N3MLMaMxuM4dMEI5ows0ozyx/cBzif4bULOQb3P7WDOxq28N5XzOG1Gp0jIkeQtATh7n3AdcCDwEbgHnd/xsxuMrPBIasXAJvM7FlgMnBzuP5kYLWZPUVQvP7qiNFPcpQ2v7iPG+5bT/2sSm64fEHU4YhIGsjouZgkcKC7j2Xf/V/aDvbwuw//A1PKNWuoiAQ0F9ME5u58+pdP09Syn5+/9xwlBxFJWNSjmCTJfvbYC9z/1A4+ccl8zjsxcybhE5HkU4LIYGu37uWLv9vAxQsmce2rXjZKWETksJQgMlTrgR4+dNcaJpcV8B9vm5iT8InI8VENIgP1DzgfXbGWPft7uO/a8ygv0s1qROToKUFkoFsfeo6/PLeHL7/xNE6rK486HBFJU+piyjCPbnqRWx9+jjedOZ3lS2Yc+QARkUNQgsgg29s6+dh/rWP+5FJufsNpmoRPRI6LEkSG6O7r51/uWkNfv/O9d55JYZ5ugykix0c1iAxx839v5KltbfzgyjOZq0n4RGQMqAWRAX6zbjt3rnyB9//DHC47VZPwicjYUIJIc8/u3scN9z3N2bMr+dRlmoRPRMaOEkQa29/dxwd//iTF+Tl85x1nkputf04RGTs6o6Qpd+ff7lvPlj0H+PbyxUwu0yR8IjK2lCDS1B0NW/jv9Tv55KXzWXpC9ZEPEBE5SkoQaejJF/Zy839v5NUnT+KDr9QkfCKSHEoQaSa2v5vr/nMNUysK+PpbNQmfiCSProNII8EkfOuIHejhl5qET0SSTC2INPKtPz3LXzfv4abXn8Kp0zUJn4gklxJEmnhk04vc+vBm3nJWHW8/W5PwiUjyKUGkgea9B7n+v9axYEopX1x2qibhE5FxkdQEYWaXmdkmM9tsZjeMsn2WmT1kZuvN7FEzq4vbdpWZPRc+rkpmnKlscBK+/n7nB1eepUn4RGTcJC1BmFk28F3gcmAhsNzMFo7Y7RbgTnc/HbgJ+Ep4bBXweeAcYAnweTOrTFasqeym325gfXM7t7ztDGbXFEcdjohMIMlsQSwBNrt7k7v3ACuAZSP2WQg8HC4/Erf9UuCP7t7q7nuBPwKXJTHWlPSrtc3c9fhWPvDKuVx6ypSowxGRCSaZCWI6sC3ueXO4Lt5TwJvC5TcCpWZWneCxmNk1ZrbazFa3tLSMWeCpYNOufXz6l0+zZE4V/3rp/KjDEZEJKOoi9SeBV5nZWuBVwHagP9GD3f02d6939/ra2tpkxTju9nX1cu3Pn6QkP5fvLF9MjibhE5EIJPNCue1A/HjMunDdEHffQdiCMLMS4M3u3mZm24ELRhz7aBJjTRmDk/C90HqQu953DpM0CZ+IRCSZP01XAfPMbI6Z5QFXAPfH72BmNWY2GMOngdvD5QeBS8ysMixOXxKuy3i3/+8WHnh6F/966XzOnatJ+EQkOklLEO7eB1xHcGLfCNzj7s+Y2U1m9vpwtwuATWb2LDAZuDk8thX4IkGSWQXcFK7LaKu3tPKVBzbymoWT+cAr50YdjohMcObuUccwJurr63316tVRh3HM2g72cOk3/0xBbjb3X/cKygs1z5KIJJ+ZPenu9aNt02R9KeIPz+xmd0c39127VMlBRFKChsekiIbGPdSU5HHmzAl5PaCIpCAliBTg7qxsirH0hBrNsyQiKUMJIgU07TnA7o5uztOtQ0UkhShBpICGxhgASzWsVURSiBJECnisMca08gJmVRdFHYqIyBAliIgNDKj+ICKpSQkiYpt276P1QA9LVX8QkRSjBBGxlYP1ByUIEUkxShARa2iMMbu6iOkVhVGHIiIyjBJEhPr6B3i8KabWg4ikJCWICD2zo4N93X0sPaEm6lBERF5GCSJCK5t0/YOIpK4jJggz+6e4ezbIGGpojHHS5BJqS/OjDkVE5GUSOfG/HXjOzL5mZguSHdBE0dM3wKrnW9V6EJGUdcQE4e5XAouBRuAOM1tpZteYWWnSo8tg65vb6OztV/1BRFJWQl1H7t4B3AusAKYCbwTWmNmHkxhbRmtojGEG586tijoUEZFRJVKDeL2Z/Qp4FMgFlrj75cAZwCeSG17mamjcw8KpZVQU5UUdiojIqBK5o9ybgW+4+5/jV7r7QTN7b3LCymxdvf2s2drGVUtnRR2KiMghJZIgbgR2Dj4xs0JgsrtvcfeHkhVYJlvzwl56+gY4T/UHEUlhidQgfgEMxD3vD9fJMWpojJGdZZw9R/UHEUldiSSIHHfvGXwSLifUcW5ml5nZJjPbbGY3jLJ9ppk9YmZrzWy9mb02XD/bzDrNbF34+EGiHygdNDTu4fS6ckryE2nAiYhEI5EE0WJmrx98YmbLgD1HOsjMsoHvApcDC4HlZrZwxG6fBe5x98XAFcD34rY1uvui8PHBBOJMC/u7+1jf3K7bi4pIykvkJ+wHgbvM7DuAAduAdyVw3BJgs7s3AZjZCmAZsCFuHwfKwuVyYEeCcaetVVta6Rtw1R9EJOUdMUG4eyNwrpmVhM/3J/ja0wmSyaBm4JwR+9wI/CG8nqIYeHXctjlmthboAD7r7n8Z+QZmdg1wDcDMmTMTDCtaKxtj5GVncdasyqhDERE5rIQ6wc3sdcApQMHgbTHd/aYxeP/lwB3u/nUzWwr8zMxOJRg1NdPdY2Z2FvBrMzslvGBviLvfBtwGUF9f72MQT9KtbIyxeGYFBbnZUYciInJYiVwo9wOC+Zg+TNDF9FYgkQH824EZcc/rwnXx3gvcA+DuK4ECoMbdu909Fq5/kmCaj5MSeM+U1n6wl7/taFf3koikhUSK1Oe5+7uAve7+BWApiZ2sVwHzzGyOmeURFKHvH7HPVuBiADM7mSBBtJhZbVjkxszmAvOApkQ+UCp77PkY7rq9qIikh0S6mLrCvwfNbBoQI5iP6bDcvc/MrgMeBLKB2939GTO7CVjt7vcTTNXxQzO7nqBgfbW7u5m9ErjJzHoJrsH4oLu3HvWnSzErG2MU5GaxaEZF1KGIiBxRIgnit2ZWAfxfYA3BifyHiby4uz8APDBi3efiljcA549y3H3AfYm8RzpZ2Rjj7NlV5OXo9hoikvoOmyDCGwU95O5twH1m9jugwN3bxyO4TNKyr5tNu/exbPG0qEMREUnIYX/KuvsAwcVug8+7lRyOzWPh7UVVoBaRdJFIX8dDZvZmGxzfKsdkZVOM0vwcTp1WduSdRURSQCIJ4gMEk/N1m1mHme0zs44jHSTDrWyMcc7cKnKyVX8QkfSQyC1HS909y93z3L0sfK6fwUdhZ3snz+85wLm6/7SIpJEjjmIKh5y+zMgbCMmhrWxU/UFE0k8iw1z/NW65gGASvieBi5ISUQZqaIxRWZTLgimlUYciIpKwRCbr+6f452Y2A/hmsgLKNO7OysYY586tJitLdX4RSR/HUjFtBk4e60Ay1dbWg2xv69T9H0Qk7SRSg/g2wdXTECSURQRXVEsCBusPS1V/EJE0k0gNYnXcch9wt7v/b5LiyTgNjTFqS/M5obY46lBERI5KIgniXqDL3fshuJWomRW5+8Hkhpb+3J2Gxhjnn1iNrjMUkXST0JXUQGHc80LgT8kJJ7M0tuxnz/5u1R9EJC0lkiAK4m8zGi4XJS+kzNEwWH+Yq/qDiKSfRBLEATM7c/BJeAvQzuSFlDkaNseYXlHIjKrCI+8sIpJiEqlBfAz4hZntILjl6BSCW5DKYQwMOI89H+M1J09W/UFE0lIiF8qtMrMFwPxw1SZ3701uWOlv464O2g72ct6Jqj+ISHo6YheTmX0IKHb3v7n734ASM/uX5IeW3laq/iAiaS6RGsT7wzvKAeDue4H3Jy2iDNHQGGNuTTFTyguiDkVE5JgkkiCy428WZGbZQF7yQkp/ff0DPPF8K0s1vFVE0lgiRer/Af7LzP6/8PkHgN8nL6T09/T2dvZ39ylBiEhaS6QF8W/Aw8AHw8fTDL9w7pDM7DIz22Rmm83shlG2zzSzR8xsrZmtN7PXxm37dHjcJjO7NLGPkxoGr3/QDYJEJJ0lcke5AeBxYAvBvSAuAjYe6biwK+q7wOXAQmC5mS0csdtngXvcfTFwBfC98NiF4fNTgMuA74WvlxYea4qxYEopNSX5UYciInLMDpkgzOwkM/u8mf0d+DawFcDdL3T37yTw2kuAze7e5O49wApg2Yh9HBi8fWk5sCNcXgascPdud38e2By+Xsrr7utn1ZZWtR5EJO0drgXxd4LWwj+6+yvc/dtA/1G89nRgW9zz5nBdvBuBK82sGXgA+PBRHIuZXWNmq81sdUtLy1GEljzrtrbR1Tug+ZdEJO0dLkG8CdgJPGJmPzSziwmupB5Ly4E73L0OeC3wMzNL+CZG7n6bu9e7e31tbe0Yh3ZsVjbFyDI4Ry0IEUlzhzwZu/uv3f0KYAHwCMGUG5PM7PtmdkkCr70dmBH3vC5cF++9wD3h+60kuOd1TYLHpqSGxhinTi+nvDA36lBERI5LIkXqA+7+n+G9qeuAtQQjm45kFTDPzOaYWR5B0fn+EftsBS4GMLOTCRJES7jfFWaWb2ZzgHnAEwl+psh09vSzdutelqr1ICIZIJHrIIaEV1HfFj6OtG+fmV0HPAhkA7e7+zNmdhOw2t3vBz4B/NDMricoWF/t7g48Y2b3ABsI7mL3ocEbFqWy1S+00tvvuv5BRDLCUSWIo+XuDxAUn+PXfS5ueQNw/iGOvRm4OZnxjbWVjTFysoyzZ1dFHYqIyHFLuCAsR9bQGOOMGRUU5yc174qIjAsliDGyr6uXp7e3a3iriGQMJYgxsmpLK/0Dqj+ISOZQghgjDZtj5OVkcebMyqhDEREZE0oQY6ShMcZZMyspyE2bKaNERA5LCWIM7D3Qw8ZdHao/iEhGUYIYA48/H8Md3X9aRDKKEsQYaGiMUZSXzel1FVGHIiIyZpQgxkBDY4yzZ1eRm62vU0Qyh85ox+nFfV1sfnG/6g8iknGUII7TyvD2orr+QUQyjRLEcVrZGKO0IIdTppVHHYqIyJhSgjhOK5tinDu3muyssb6XkohItJQgjkPz3oO8EDuo+z+ISEZSgjgOg/UHXf8gIplICeI4rGyKUV2cx0mTSqMORURkzClBHCN3Z2VjjHNPqCZL9QcRyUBKEMdoS+wgO9u7VH8QkYylBHGMGhr3AOgCORHJWEoQx2hlY4wpZQXMqSmOOhQRkaRQgjgGg/WHpSdUY6b6g4hkpqQmCDO7zMw2mdlmM7thlO3fMLN14eNZM2uL29Yft+3+ZMZ5tJ7dvZ/YgR5NryEiGS0nWS9sZtnAd4HXAM3AKjO73903DO7j7tfH7f9hYHHcS3S6+6JkxXc8Vqr+ICITQDJbEEuAze7e5O49wApg2WH2Xw7cncR4xkxDY4wZVYXUVRZFHYqISNIkM0FMB7bFPW8O172Mmc0C5gAPx60uMLPVZvaYmb3hEMddE+6zuqWlZYzCPrz+Aeexphjnza0Zl/cTEYlKqhSprwDudff+uHWz3L0eeAfwTTM7YeRB7n6bu9e7e31tbe24BLpxZwcdXX2aXkNEMl4yE8R2YEbc87pw3WiuYET3krtvD/82AY8yvD4RmcHrH3SBnIhkumQmiFXAPDObY2Z5BEngZaORzGwBUAmsjFtXaWb54XINcD6wYeSxUWhojHFCbTGTygqiDkVEJKmSliDcvQ+4DngQ2Ajc4+7PmNlNZvb6uF2vAFa4u8etOxlYbWZPAY8AX40f/RSV3v4Bnni+lfNOUP1BRDJf0oa5Arj7A8ADI9Z9bsTzG0c5rgE4LZmxHYv1ze0c7OnX8FYRmRBSpUidFgavfzhH9QcRmQCUII5CQ2OMk6eWUVWcF3UoIiJJpwSRoK7efp58Ya+6l0RkwlCCSNDarW109w1oeKuITBhKEAla2biHLIMlc6uiDkVEZFwoQSSooTHGaXUVlBXkRh2KiMi4UIJIwMGePtZta1P9QUQmFCWIBKzaspe+AVf9QUQmFCWIBDQ07iE326ifXRl1KCIi40YJIgGPNcZYPKOSorykXnguIpJSlCCOoL2zl6e3t3Ou6g8iMsEoQRzBE8+3MuC6vaiITDxKEEewsjFGfk4Wi2dWRB2KiMi4UoI4gobGPdTPriQ/JzvqUERExpUSxGHE9nfz9137dP8HEZmQlCAO47GmVgCWqv4gIhOQEsRhrGzaQ0l+DqdPL486FBGRcacEcRgNjTHOnl1JTra+JhGZeHTmO4TdHV00tRxQ/UFEJiwliENY2RgDVH8QkYlLCeIQGhr3UF6Yy8KpZVGHIiISiaQmCDO7zMw2mdlmM7thlO3fMLN14eNZM2uL23aVmT0XPq5KZpyjaWiMce7cKrKybLzfWkQkJSRt9jkzywa+C7wGaAZWmdn97r5hcB93vz5u/w8Di8PlKuDzQD3gwJPhsXuTFW+8ba0Had7byfv/Ye54vJ2ISEpKZgtiCbDZ3ZvcvQdYASw7zP7LgbvD5UuBP7p7a5gU/ghclsRYh1H9QUQkuQliOrAt7nlzuO5lzGwWMAd4+GiONbNrzGy1ma1uaWkZk6AhqD/UlOQxb1LJmL2miEi6SZUi9RXAve7efzQHuftt7l7v7vW1tbVjEoi709AYY+kJNZip/iAiE1cyE8R2YEbc87pw3Wiu4KXupaM9dkw17TnAi/u6Nb23iEx4yUwQq4B5ZjbHzPIIksD9I3cyswVAJbAybvWDwCVmVmlmlcAl4bqkaxisP+j+0yIywSVtFJO795nZdQQn9mzgdnd/xsxuAla7+2CyuAJY4e4ed2yrmX2RIMkA3OTurcmKNd7Kxj1MKy9gVnXReLydiEjKSupNlt39AeCBEes+N+L5jYc49nbg9qQFN4qBAeexplYunD9J9QcRmfBSpUidEjbt3kfrgR4NbxURQQlimAZd/yAiMkQJIs7Kxhizq4uYXlEYdSgiIpFTggj19Q/weFNMrQcRkZASROiZHR3s6+5jqe7/ICICKEEM0fUPIiLDKUGEVjbFOGlyCbWl+VGHIiKSEpQggJ6+AVY936rWg4hIHCUI4KnmNjp7+1V/EBGJowRBMLzVDM6dWxV1KCIiKUMJguD+DwunllFRlBd1KCIiKWPCJ4iu3n7WvNCm6b1FREaY8Amio6uXy06dwoULJkUdiohISknqbK7pYFJpAbcuXxx1GCIiKWfCtyBERGR0ShAiIjIqJQgRERmVEoSIiIxKCUJEREalBCEiIqNSghARkVEpQYiIyKjM3aOOYUyYWQvwQtRxHKcaYE/UQaQQfR/D6ft4ib6L4Y7n+5jl7rWjbciYBJEJzGy1u9dHHUeq0PcxnL6Pl+i7GC5Z34e6mEREZFRKECIiMioliNRyW9QBpBh9H8Pp+3iJvovhkvJ9qAYhIiKjUgtCRERGpQQhIiKjUoJIAWY2w8weMbMNZvaMmX006piiZmbZZrbWzH4XdSxRM7MKM7vXzP5uZhvNbGnUMUXJzK4P/z/5m5ndbWYFUcc0nszsdjN70cz+Freuysz+aGbPhX8rx+K9lCBSQx/wCXdfCJwLfMjMFkYcU9Q+CmyMOogU8S3gf9x9AXAGE/h7MbPpwEeAenc/FcgGrog2qnF3B3DZiHU3AA+5+zzgofD5cVOCSAHuvtPd14TL+whOANOjjSo6ZlYHvA74UdSxRM3MyoFXAj8GcPced2+LNKjo5QCFZpYDFAE7Io5nXLn7n4HWEauXAT8Nl38KvGEs3ksJIsWY2WxgMfB4xKFE6ZvAp4CBiONIBXOAFuAnYZfbj8ysOOqgouLu24FbgK3ATqDd3f8QbVQpYbK77wyXdwGTx+JFlSBSiJmVAPcBH3P3jqjjiYKZ/SPwors/GXUsKSIHOBP4vrsvBg4wRt0H6SjsW19GkDinAcVmdmW0UaUWD65dGJPrF5QgUoSZ5RIkh7vc/ZdRxxOh84HXm9kWYAVwkZn9PNqQItUMNLv7YIvyXoKEMVG9Gnje3VvcvRf4JXBexDGlgt1mNhUg/PviWLyoEkQKMDMj6GPe6O7/EXU8UXL3T7t7nbvPJig+PuzuE/YXorvvAraZ2fxw1cXAhghDitpW4FwzKwr/v7mYCVy0j3M/cFW4fBXwm7F4USWI1HA+8M8Ev5bXhY/XRh2UpIwPA3eZ2XpgEfDlaMOJTtiSuhdYAzxNcA6bUNNumNndwEpgvpk1m9l7ga8CrzGz5whaWV8dk/fSVBsiIjIatSBERGRUShAiIjIqJQgRERmVEoSIiIxKCUJEREalBCFyFMysP24o8jozG7Orms1sdvwMnSJRy4k6AJE00+nui6IOQmQ8qAUhMgbMbIuZfc3MnjazJ8zsxHD9bDN72MzWm9lDZjYzXD/ZzH5lZk+Fj8HpIrLN7Ifh/Q7+YGaFkX0omfCUIESOTuGILqa3x21rd/fTgO8QzEgL8G3gp+5+OnAXcGu4/lbg/7n7GQRzKz0Trp8HfNfdTwHagDcn9dOIHIaupBY5Cma2391LRlm/BbjI3ZvCiRd3uXu1me0Bprp7b7h+p7vXmFkLUOfu3XGvMRv4Y3jTF8zs34Bcd//SOHw0kZdRC0Jk7Pghlo9Gd9xyP6oTSoSUIETGztvj/q4Mlxt46ZaY7wT+Ei4/BFwLQ/ffLh+vIEUSpV8nIken0MzWxT3/H3cfHOpaGc642g0sD9d9mOBucP9KcGe4d4frPwrcFs7E2U+QLHYikkJUgxAZA2ENot7d90Qdi8hYUReTiIiMSi0IEREZlVoQIiIyKiUIEREZlRKEiIiMSglCRERGpQQhIiKj+v8BpwL0AWhzWEwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_learningCurve(history,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mlxtend.plotting import plot_confusion_matrix\n",
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict_classes(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 504x504 with 1 Axes>,\n",
       " <AxesSubplot:xlabel='predicted label', ylabel='true label'>)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mat = confusion_matrix(y_test, y_pred)\n",
    "plot_confusion_matrix(conf_mat=mat, class_names=label.classes_, show_normed=True, figsize=(7,7))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d (Conv2D)              (None, 79, 2, 16)         80        \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 79, 2, 16)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 78, 1, 32)         2080      \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 78, 1, 32)         0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 2496)              0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 64)                159808    \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 6)                 390       \n",
      "=================================================================\n",
      "Total params: 162,358\n",
      "Trainable params: 162,358\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save_weights('model.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved model to disk\n"
     ]
    }
   ],
   "source": [
    "# serialize model to JSON\n",
    "model_json = model.to_json()\n",
    "with open(\"model.json\", \"w\") as json_file:\n",
    "    json_file.write(model_json)\n",
    "# serialize weights to HDF5\n",
    "model.save_weights(\"model.h5\")\n",
    "print(\"Saved model to disk\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "python3.6(tensorflow)",
   "language": "python",
   "name": "tensorflow"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
